Billing System Using Machine Learning Techniques - Raviteja, Bongu; Zargar, Mohammad Adnan; Kumar, Sandeep (International Journal of Computer Sciences and Engineering, 2023)
The Procedure for utilizing premade standardized tags to perceive a thing during charging process is drawn-out and work through .The most common way of checking the every single item takes additional time and charging the bill and giving it to the client requires greater investment . This requires part of handling the work on the items to prepare them for ID and grouping. This paper presents an elective framework that chips away at the standard of self-checking of items and computerization includes that consequently produces the bill to such an extent that there will be no wastage of time To execute this framework we want to create some distance from customary techniques for programming and utilize an alternate worldview for planning the scanner which should be able to connect it with data base which stores the information of the products .we use machine learning model implemented in-order to perform the data in libraries like cv, numpy, pyzbar and Front-end using Tkinter and a Database for Mysql and connection of the Mysql with python. This paper describes about the disengaged programming System from charging the process without worrying about the gear Environment. We pick python and Tkinter to design the qr or normalized ID scanner and the front end for the customized show and SQL for the capacity of the information of the items to execute the framework over a circulated network inside any Estabilishment that needs to integrate this cycle so every hub that needs to handle charging need that needs to deal with charging need not need to stick to the equipment prerequisite forced on them to run the different models dependent on the GPU-based tensor engineering of tensor Stream.